{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grouping objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `Group` class is used to group items together.  This allows you to then manipulate the whole Group as a single object.  This is similar to the \"selection\" and \"group\" functionality in Inkscape/Adobe Illustrator, where you can highlight several objects (by e.g. shift-clicking) and move them together.\n",
    "\n",
    "You can manipulate a Group like any other phidl object, such as:\n",
    "\n",
    "- Direct manipulation: `move()`, `rotate()`, and `mirror()`\n",
    "- Arrange with `align()` and  `distribute()`\n",
    "- and the usual list of attributes (`xmin`, `ymax`, `center`, `bbox`, etc)\n",
    "\n",
    "## Creating and manipulating a Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import phidl.geometry as pg\n",
    "from phidl import quickplot as qp\n",
    "from phidl import Device\n",
    "\n",
    "from phidl import Group\n",
    "\n",
    "# Create a blank Device and add number shapes to it\n",
    "D = Device()\n",
    "t1 = D << pg.text('1')\n",
    "t2 = D << pg.text('2')\n",
    "t3 = D << pg.text('3')\n",
    "t4 = D << pg.text('4')\n",
    "t5 = D << pg.text('5')\n",
    "t6 = D << pg.text('6')\n",
    "\n",
    "# Spread out devices\n",
    "D.distribute(direction = 'x', spacing = 3)\n",
    "\n",
    "qp(D) # quickplot it!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Say we want to only move the even numbers. We can create a group of those numbers and move them them up in the Y direction a few units easily:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "even = Group([t2,t4,t6])\n",
    "even.movey(5)\n",
    "\n",
    "qp(D) # quickplot it!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's make a Group out of the odd numbers so we can rotate them by 90 degrees.  We're going to do make this Group in a slightly different way -- simply by using the `+` operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "odd = t1 + t3 + t5 # Create the Group\n",
    "odd.rotate(-90)    # Rotate the Group\n",
    "\n",
    "qp(D) # quickplot it!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Any PHIDL object can be  summed in this way to create a Group -- this includes Device, DeviceReference, Port, Polygon, CellArray, and Label. Groups themselves can also be summed. \n",
    "\n",
    "We can even add items to groups if we need to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_to_five = t1 + t2    # Create the group\n",
    "one_to_five.add([t3,t4]) # Add more elements with the \"add\" method\n",
    "one_to_five += t5        # Equivalently, add more elements with the '+=' operator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also perform the usual manipulations of PHIDL objects like asking where the center is, xmin, ymax, etc. Here we move the entire `one_to_five` group (which now has all numbers except for 6) down so the top is aligned with the y==0 axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(one_to_five.ymax)\n",
    "one_to_five.ymax = 0\n",
    "\n",
    "qp(D) # quickplot it!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Align and distribute in Groups\n",
    "Lastly, we can also `align()` and `distribute()` a Group if we want to.  Let's start with some misaligned objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a blank Device and add number shapes to it\n",
    "D = Device()\n",
    "t1 = D << pg.text('1').move([-5,-5])\n",
    "t2 = D << pg.text('2').move([10,-15])\n",
    "t3 = D << pg.text('3').move([20, 5])\n",
    "t4 = D << pg.text('4').move([30, -7])\n",
    "t5 = D << pg.text('5').move([50, -13])\n",
    "t6 = D << pg.text('6').move([60,6])\n",
    "\n",
    "qp(D) # quickplot it!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By forming a Group out of all the objects and using the `align()` command, we can easily align them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_numbers = (t1+t2+t3+t4+t5+t6)     # Make a Group of all the numbers \n",
    "all_numbers.align(alignment = 'ymax') # Align the ymax of all the numbers\n",
    "\n",
    "qp(D) # quickplot it!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's aligned now, but still not evenly distributed.  Let's fix that by using the `distribute()` command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_numbers.distribute(direction = 'x', spacing = 1.2, separation = True)\n",
    "\n",
    "qp(D) # quickplot it!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can even change the order of distribution -- it's determined by the order the objects are added to the Group.  If we want to reverse the order of the numbers, we simply need to make a new Group with the correct order:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_numbers_reverse = (t6 + t5 + t4 + t3 + t2 + t1) # Start with t6, then t5, ...\n",
    "all_numbers_reverse.distribute(direction = 'x', spacing = 1.2, separation = True)\n",
    "\n",
    "qp(D) # quickplot it!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
